LATEST ANALYSIS

In our latest analysis, we used our music-focused algorithm to predict Spotify and YouTube streaming numbers for five artists in the run-up to Midem 2018. Read the full post, or scroll down to learn more about how we predict commercial performance and earning potential of new music.

WHAT WE DO

Hyperlive creates unique, data-driven value for artists, record labels, music repositories, and rights-holders. Our algorithm models a range of neurobiobehavioural responses to music in the population-at-large, allowing us to quantify musical engagement on a fundamental, multi-sensory level. Our algorithm can be used to predict commercial potential of new songs, develop sync/licensing opportunities, value copyright collections, and much more. Scroll down for some example data, check out our Spotify predictions for BBC Music artists at SXSW 2018, or discover how accurately we predicted streams and sales performance of $1B-worth of hit songs.

To demonstrate how accurately we can forecast a song's commercial performance — and thus it's earning potential — we predicted how many streams+sales each single released from Taylor Swift's most successful album to date, 1989, was likely to have amassed. Based entirely on an analysis of each track's audio signature, we were able to predict its actual performance with 84% accuracy. Streams and sales figures represent combined Spotify and YouTube streams plus stream-equivalent single sales.

MODELLING MUSICAL ENGAGEMENT

We model a range of neurological, physiological and behavioural responses to music as well as the psychological processes that underpin them. This gives us a deep understanding of what drives musical engagement on a fundamental level, allowing us to quantify, model and predict that engagement — and the musical features that motivate it — with unmatched levels of precision.

To highlight the effectiveness of our algorithm across different genres, we predicted how many streams+sales each track released as a single from Beyoncé's third solo album, I Am... Sasha Fierce, was likely to have amassed. By analysing nothing more than each track's audio signature, we were able to predict its actual performance with 83% accuracy. Streams and sales figures represent combined Spotify and YouTube streams plus stream-equivalent single sales.

QUANTIFYING EARNING POTENTIAL

A major application of our model is in predicting the earning potential of new songs. Because our algorithm models fundamental, mass-scale responses to music, we can predict to a high degree of accuracy how successful a song is likely to be before it's released. This helps minimise risk and maximise return on what’s often a substantial investment. In the process, copyrights associated with these more successful tracks naturally increase in value.

To further illustrate the accuracy of our algorithm, we predicted how many streams+sales each track released as a single from Drake's most recent album, Views, was likely to have amassed. Based entirely upon its audio signature, we were able to predict each track's actual performance with 88% accuracy. Streams and sales figures represent combined Spotify and YouTube streams plus stream-equivalent single sales.

SYNC, BRANDING, GAMING AND MORE

Our algorithm has a range of additional applications across the broader music industry, from sync and branding to gaming and copyright-valuation. Contact us to learn more.